Investigating the mechanism of ShuFeng JieDu capsule for the treatment of novel Coronavirus pneumonia (COVID-19) based on network pharmacology

ShuFeng JieDu capsule (SFJDC), a traditional Chinese medicine, has been recommended for the treatment of COVID-19 infections. However, the pharmacological mechanism of SFJDC still remains vague to date. The active ingredients and their target genes of SFJDC were collected from TCMSP. COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). NCP-related target genes were collected from GeneCards database. The ingredients-targets network of SFJDC and PPI networks were constructed. The candidate genes were screened by Venn diagram package for enrichment analysis. The gene-pathway network was structured to obtain key target genes. In total, 124 active ingredients, 120 target genes of SFJDC and 251 NCP-related target genes were collected. The functional annotations cluster 1 of 23 candidate genes (CGs) were related to lung and Virus infection. RELA, MAPK1, MAPK14, CASP3, CASP8 and IL6 were the key target genes. The results suggested that SFJDC cloud be treated COVID-19 by multi-compounds and multi-pathways, and this study showed that the mechanism of traditional Chinese medicine (TCM) in the treatment of disease from the overall perspective.


Introduction
Since December 2019, a novel coronavirus pneumonia (NCP) caused by new coronavirus (SARS-COV-2) has been prevalent in China and other countries, such as United States and Korea [1][2][3]. WHO named this novel coronavirus pneumonia COVID-19 on February 11, 2020 [4] and there was a total of 20 million reported cases of COVID-19 globally and 750,000 deaths as of August 10, 2020 [5].
Its transmission route is mainly through respiratory droplets, but also through contact transmission, which has the characteristics of rapid spread, strong infectivity and general susceptibility of various groups of people. COVID-19 mild patients present with fever, fatigue, dry cough and other symptoms, whereas severe patients can appear with dyspnea, acute respiratory distress syndrome (ARDS) or septic shock and other symptoms. There is no special drug at present [6,7].
The treatment of COVID-19 mainly consisted of bed rest; intensive supportive treatment; oxygen therapy; antiviral therapy; antimicrobial therapy and Chinese medicine treatment. Critical cases need respiratory support (high flow nasal oxygen therapy, non-invasive ventilator or invasive mechanical ventilator); circulatory support for critically ill patients; plasma treatment from recovered patients and immunotherapy [8,9]. Most of the infectious diseases caused by virus belong to the category of "plague" in ancient Chinese traditional medicine, which is caused by many evil spirits invading the Ivyspring International Publisher body [10]. The traditional medicine, including traditional Chinese medicine (TCM), has a good therapeutic effect on it [11,12]. The Health and Health Commission of China and the State Administration of traditional Chinese Medicine in the "circular on the issuance of a new type of coronavirus infection pneumonia diagnosis and treatment program (version 5)" requested to strengthen the integration of Chinese and western medicine, and recommended a number of proprietary Chinese medicine in the process of diagnosis and treatment [13]. On the basis of the national plan and in accordance with the principle of "three conditions and conditions", local prevention and control projects have also been successively issued according to local conditions [14]. Recommended Chinese medicines include MaXing ShiGan Tang, QingFei PaiDu Tang, HuoXiang ZhengQi Capsules, JinHua QingGan Granules, LianHua QingWen Capsules or ShuFeng JieDu capsule [8]. One clinical study showed that LianHua QingWen could improve the symptoms of COVID-19 patients and shorten the course of disease [15]. A retrospective analysis study showed that the time of disappearance of clinical symptoms, recovery of body temperature, average length of stay in the integrated Chinese and western medicine treatment group (34) was significantly lower than that of the western medicine group (18) among the 52 COVID-19 patients [16].With QingFei PaiDu Tang combined with western medicine to treat the COVID-19 could significantly improve the patient's symptoms and achieved better results [17].
Network pharmacology is a new discipline based on the theory of system biology, which analyzes the biological systems and selects specific signal nodes for multi-target drug molecular design. Network pharmacology emphasizes the multipathway regulation of signaling pathways and the regulation of multi-component, multi-target, multipathway, linking active components in traditional chinese medicine with target genes from molecular and biological aspects [23]. Network pharmacology will help to understand the relationship among ingredients, genes and diseases and is suitable for the study of complex TCM or TCM compounds. The potential mechanism of preventing COVID-19 by HuoXiang ZhengQi oral solution was realized by network pharmacology and molecular docking [24]. The research group Jing Zhao elucidated the mechanism of QingFei PaiDu Tang in the treatment of COVID-19 using network pharmacology [25]. SFJDC could be efficacious for COVID-19, but active incredients, target genes and putative mechanism are not known. In the present study, the network pharmacological was used to investigate the possible mechanism and target of SFJDC in the treatment of COVID-19. COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). The active ingredients and their target genes of SFJDC were collected from TCMSP. NCP-related target genes were collected from GeneCards database. The putative mechanism of SFJDC against NCP were analyzed by GO and KEGG pathway. The flowchart of network pharmacology was shown in Figure 1. The study provided possible theoretical reference for SFJDC in the prevention and treatment of COVID-19.

Screening of active Ingredients in SFJDC
We identified the active ingredients of SFJDC from Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP http://tcmspw.com/tcmsp.php) [26]. TCMSP is a unique herbal pharmacology platform that captures the relationship between drugs, target genes and diseases. The database includes the detection of natural compounds such as chemical, target and drug target networks. ADME is pharmacokinetics, which refers to the absorption, distribution, metabolism and excretion of exogenous chemicals by myosome. The four key parameters of ADME were blood-brain barrier (BBB), oral bioavailability (OB), Caco-2 permeability (Caco-2) and drug-likeness (DL) [27]. In this study select candidate compounds which has OB≥30%, DL≥ 0.18, Caco-2≥-0.4, BBB≥-0.3.Then we sorted out each active ingredient for identification of targets.

Identification of SFJDC putative target genes
This study used the TCMSP platform to obtain the putative target genes of active ingredients of SFJDC. The Uniprot (https://www.uniprot.org/) [28] database provides a comprehensive, high quality and freely available source of protein sequence and function information. The putative target information corresponding to the active ingredients were input into UniProt database to obtain the standard name of the action target genes.

Screening of NCP related targets
COVID-19 is a type of Novel Coronavirus Pneumonia (NCP). So We collected NCP related targets from GeneCards (https://www.genecards. org/), which is a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes [29]. The key word "Novel Coronavirus Pneumonia" was used in the GeenCards database.

PPI (Protein-Protein Interaction) network construction of SFJDC putative and NCP related target genes
The PPI network of SFJDC putative and NCP related targets would be obtained from STRING (https://string-db.org/ ver11.0, update Jan 2019) [30]. Active interaction sources were set as follows: Text-mining, Co-expression, Neighborhood, Experiments, Databases, Gene Fusion and Co-occurrence. The required minimum interaction score was set at 0.4 in PPI network of SFJDC related targets, PPI network of NCP was set at 0.9. The barplot were generated by the R software (https://www.r-project.org/ver 3.6.2) based on counts value.

Construction of SFJDC ingredient-target network
Perl (https://www.perl.org/get.html) is a programming language suitable for writing simple scripts as well as complex applications. We used Strawberry Perl 5.30.1.1 to prepare the ingredienttarget network. Cytoscape is a universal open source software for large-scale integrated development of molecular interaction networks working data. Then the ingredients-targets network of SFJDC was constructed using Cytoscape 3.7.2 software [31].

PPI network construction of SFJDC against NCP
In order to reveal the mechanism of SFJDC against NCP, a PPI network was constructed by the BisoGenet client which is a Cytoscape plugin was used to visualize. In this plugin, Protein-protein interactions information is taken from the DIP, BIOGRID, HPRD, INTACT, MINT, BIND [32]. CytoNCA is a Cytoscape plugin integrating calculation, evaluation and visualization analysis for multiple centrality measure measures including Betweenness Centrality (BC), Degree Centrality (DC), Colseness Centrality (CC), Local average connectivity-based method (LAC), Eigenvector Centrality (EC) and Network Centrality (NC) [33].

Identification of candidate genes (CGs) and enrichment analysis of CGs
The CGs were filtered with R software using the Venn Diagram package (https://cran.r-project.org/ web/packages/VennDiagram/index.html). The CGs would be used for Gene Ontology (GO) analysis (including biological processes (BP), molecular functions (MF), and cellular components (CC)) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO and KEGG pathway analyses results were processed by the "enrichplot" (http://www. bioconductor.org/packages/release/bioc/html/enric hplot.html) "clusterProfiler" (http://www. bioconductor.org/packages/release/bioc/html/clust erProfiler.html) and "ggplot2" packages by R software. A P value of less than 0.05 was used regarded as statistically significant. At the same time, we input CGs into DAVID (https://david.ncifcrf. gov/) for functional enrichment analysis to obtain disease clustering.

Construction of gene-pathway network
KEGG pathways that had significant changes of P<0.05 were further analyzed. The genes that significantly regulated pathways for gene-pathway network construction. The key target genes of SFJDC against NCP were screened by gene-pathway network.

The active ingredients of each herb contained in SFJDC
One hundred and thirty-seven active ingredients were screened out of TCMSP based on ADME, 4 in PCRR, 17 in FF, 25 in IR, 9 in HP, 7 in PR, 7 in VH, 1 in I, 67 in RB and 13 of which were repeated. Finally, 124 candidate active components of each herb contained in SFJDC were screened for further analysis after removing duplation ( Table 2).

Putative target genes of each herb in SFJDC and NCP related target genes
The 124 candidate active components were imported into TCMSP database and Uniport database to identify the Putative target genes of each herb in SFJDC. One hundred and ten components were finally selected after removing 14 ingredients which did not link to any target genes. The target genes of 110 compounds were collected. 1705 genes were identified, 103 in PR, 209 in IR, 65 in HP, 1052 in RB, 75 in PCRR, 173 in FF and 27 in I. There were 1585 genes of the eight herbs overlapped, which was suggestive of potential interaction between the compounds of SFJDCA in the course of treatment. A total of 120 genes were identified after removing duplation ( Table 3). And 251 NCP related target genes were identified from Gene Cards database ( Table 4).

PPI network of SFJDC putative and NCP related target genes
In this study, we constructed the PPI network of SFJDC putative and NCP related target genes separately. The network of SFJDC putative target genes which minimum interaction score was set at 0.4 contained 119 nodes and 1108 edges which indicated the target genes interactions after removing the discrete points (Figure 2A). According the PPI network, the top thirty genes were listed in Figure 2B. After hiding the discrete points, NCP-related target genes PPI network contained 248 nodes and 1235 edges ( Figure 2C). Similarly, the first 30 related genes were shown in Figure 2D.

SFJDC ingredient-target network analysis
The ingredient-target network of SFJDC was constructed using the screened ingredients and their targets as shown in Figure 3. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. A median of 110 candidate compouds was 5 degrees which indicating that most compounds of SFJDC were affected by multiple target genes. The top three effective ingredient according were Wogonin, licochalcone a and acacetin. Wogonin, licochalcone a and acacetin have 42, 30 and 23 target genes, respectively. And the OB of Wogonin, licochalcone a and acacetin were 30.68, 40.79 and 34.97%, respectively. Hence, they might be the crucial effective compounds of SFJDC according the network.

PPI network analysis of SFJDC against NCP
PPI network of SFJDC against NCP were visualized using Cytoscape software. The network contained 2407 nodes and 53639 edges was shown in Figure 4A. The average degree of all nodes was 44.5692 and we selected the nodes with more than 44.5692 degrees as significant genes. A subnetwork of significant genes for SFJDC against NCP was constructed which consisted of 766 nodes and 28872 edges ( Figure 4B). The average value of BC was 711.9504. The significant genes were further screened and a new network was constructed with 169 nodes and 4238 edges ( Figure 4C). 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.

Identification of candidate genes (CGs) and Enrichment analysis of CGs
Twenty-three candidate genes (CGs) were identified by using the VennDiagram package ( Figure  5). Then R software was used to perform GO and KEGG pathway analysis of the CGs. GO of CGs was analyzed based on BP, CC, MF. 1215 GO terms were significantly enriched (P<0.05), 1148 in BP, 28 in CC, 39 in MF. Top 20 terms were shown in Figure 6. The data of top 20 GO analysis were listed in Table 5. Based on these GO terms data, we found that most significantly terms were response to lipopolysaccharide, response to molecule of bacterial origin, membrane raft, membrane microdomain, BH domain binding and death domain binding, suggested that SFJDC could treat NCP with multiple synergies.
The pathways that were significantly affected by SFJDC in the process of treating NCP were identified by KEGG pathway. 110 KEGG pathways were significantly enriched (P<0.05). Top twenty pathways were shown in Figure 7, color represented P value and size of the spot represented count of genes. Based on the analysis of KEGG pathway data ( In this study, we chose the functional annotation clustering and set the classification stringency as high in DAVID. A total of 20 functional annotation clusters were obtained ( Table 7). Annotation Cluster 1 (enrichment score 6.04) contains three categories: Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis, and all of them were lung related diseases and Virus infection disease.

Gene-pathway network analysis
The construction of gene-pathway network is based on significant enrichment pathway and regulated these ways, which was shown in Figure 8. The V shapes represented pathway and the squares represent target genes in the network. The network showed that RELA was the core target gene which had largest degree. Other five genes also had larger degree such as MAPK1, MAPK14, CASP3, CASP8 and IL6. They might be the key target genes using SFJDC in the process of treating NCP. All of the above analysis could reveal a new strategy for drug development on NCP.

Discussion
The theory of TCM has been formed and developed for thousands of years in China. In China, TCM has a good therapeutic effect on COVID-19, which has been written into the diagnosis and treatment guidelines. The guideline points out that the combination of traditional Chinese and western medicine should be strengthened in the treatment process [34]. SFJDC is a traditional Chinese medicine, mainly used to treat upper respiratory tract infections, such as influenza, sore throat, mumps, streptococcus, etc. [21]. Now, SFJDC has become an effective drug for the treatment of COVID-19 [35]. In recent years, the research on Chinese medicine prescriptions has developed to the level of effective parts, components, components. Network pharmacology can better understand and demonstrate the interaction between multi-component multi-target and disease [36]. This study aims to analyze the active components and potential mechanism of SFJDC in the treatment of COVID-19 through network pharmacology.
In the present study, the ingredients-targets network of SFJDC was constructed using 110 ingredients and 120 targets. The network contained 117 nodes and 419 edges which indicated the compound-target genes interaction. The results showed that most compounds of SFJDC were affected by multiple target genes, such as Wogonin, licochalcone a and acacetin acted on 42, 30 and 23 target genes, respectively. Various compounds of SFJDC may have the same targets to achieve synergy. Wogonin, a naturally occurring flavonoid, has been shown to multi-activity, such as anti-inflammatory, anti-fibrosis, anti-cancer and chondroprotective properties [37]. Study showed that wogonin had an anti-infulenza activity by modulation of AMPK pathway [38]. Licochalcone a, a flavonoid extracted from licorice toot, was known for its anti-inflammatory, anti-cancer, anti-oxidative and anti-bacterial bioactivity [39]. Acacetin, a flavone compound, played an important role in antiinflammatory and anti-peroxidative [40].     In addition, they have high OB and acacetin from 2 herbs (PR, IR) of SFJDC. The three main ingredients were anti-inflammatory and COVID-19 caused by a series of inflammatory storms. Hence, they might be the crucial effective compounds of SFJDC according the network.
PPI network of SFJDC against NCP were visualized using Cytoscape software to obtain the candidate target genes. In order to obtain the more accurate genes, two parameters including DC and BC were used to screen nodes and structure a new network. 169 genes were eventually identified for SFJDC against NCP including 156 other human genes and 13 target genes.
Twenty-three candidate genes (CGs) were identified by using the VennDiagram package. CGs were enriched in BP, CC, MF by GO enrichment analysis. Based on GO terms data, we found that some terms were response to lipopolysaccharide or bacterial origin, membrane raft, membrane microdomain, BH domain binding and cytokine receptor binding. COVID-19 infections leaded to a strong immune response and inflammatory storm in which a large number of cytokines were activated, so SFJDC might regulate COVID-19 through the above biological processes.
SFJDC, as a TCM formula, has multi-component, multi-target-gene, multi-pathway. In the present study, 110 KEGG pathways were significantly enriched. Seven of the top 20 pathways were associated with viral infection including Kaposi sarcoma-associated herpesvirus infection, Human cytomegalovirus infection, Hepatitis B, Influenza A, Epstein-Barr virus infection, Human immunodeficiency virus 1 infection and Measles, and three were associated with lung disease contained tuberculosis, pertussis and small cell lung cancer. Multiple targets of SFJDC may also inhibit the activation of cytokines and reduce inflammation by regulating cytokine pathways, such as IL-17 signaling pathway and TNF signaling pathway. In this study, we obtained 20 functional annotation clusters through DAVID. Annotation Cluster1 including Asthma, Bronchiolitis Viral, Respiratory Syncytial Virus Infections, respiratory syncytial virus bronchiolitis were lung related diseases and Virus infection disease.
Gene-pathway network was constructed to the core and key target genes. The network showed that RELA had largest degree, was the core target gene. Other top five genes such as MAPK1, MAPK14, CASP3, CASP8 and IL6 might be the key target genes. The pathophysiological process of Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-COV-2) infection is similar to that of SARS-CoV infection, with a strong inflammatory response. The SARS-COV-2 virus mainly targets respiratory epithelial cells, alveolar epithelial cells, vascular endothelial cells and pulmonary macrophages, all of which express Angiotensin converting enzyme 2 (ACE2) receptor, triggering the generation of proinflammatory cytokines and chemokines (including IL-6, TNF, IL-10 and MCP1) [41]. The NF-kB family member RELA is a widely expressed and effective transcriptional activator that activates the expression of many inflammatory through exposure to pathogens and inflammatory cytokines [42]. RELA may play an important role in the infection of COVID-19. MAPK1 and MAPK14 are members of the MAPK family, which can regulate multiple cellular processes, such as response to oxidative stress, antiinflammatory, immune response, apoptosis and cell proliferation [43]. Joseph et al showed SASR-CoV-2 could induce severe inflammation by directly activating p38 MAPK pathway and many p38 MAPK inhibitors are in the clinical stage and should be considered for clinical trial for severe COVID-19 infection [44]. CASP3 and CASP8, a family of cysteine-dependent proteases, play an important role in these events through activation of other apoptotic proteins mediated by proteolysis and cleavage of nuclear proteins [45]. In Krahling's study, infection of 293/ACE2 cells with SARS-CoV activated apoptosisassociated events, such as caspase3, caspase 8 [46]. Therefore, we conclude that CASP3 and CASP8 may be activated and play an important role in the pathophysiological process of COVID-19. Higher plasma level of IL-6 was found in ICU patients with COVID-19 [47].
Tocilizumab, a recombinant humanized anti-human IL-6 receptor monoclonal antibody, improved the clinical outcome in 20 severe and critical COVID-19 patients and is an effective treatment to reduce mortality [48].   It has been clinically confirmed that SFJDC is effective in the treatment of COVID-19. Wang et al shown that conventional treatment combined with SFJDC treatment for 4 cases of COVID-19 patients could significantly improve symptoms and promote viral negative conversion [49]. Another study including 70 COVID-19 patients found that SFJDC combined with Arbidol for COVID-19 compared with single using Arbidol could significantly shorten the time of clinical symptoms improvement and COVID-19 negative conversion [50].
To summarise, the compound and targets of SFJDC were systematically studied by applying network pharmacology. Wogonin, licochalcone a and acacetin regulated the most targets associated with NCP. RELA, MAPK1, MAPK14, CASP3, CASP8 and IL6 were the core and key genes in the gene-network of SFJDC for the treatment of NCP. SFJDC regulated novel coronavirus pneumonia by multi-compound and multi-target, which provided theoretical support for SFJDC against COVID-19. More mechanism and roles require further clinical validation.